Exploiting Spectral and Spatial Information in Hyperspectral Urban Data With High Resolution

Dell’Acqua, Fabio; Gamba, Paolo; Ferrari, Alberto; Palmason, J.A.; Benediktsson, Jón Atli; Arnason, K.

doi:10.1109/lgrs.2004.837009

Cited by 217 publications

(83 citation statements)

References 16 publications

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“…Conventionally, the first spectral principal component of multispectral data is extracted to build a set of spatial features [2], [8]. But in this letter, all the spectral bands are used to extract the spatial information, after which an unsupervised feature selection approach using the feature similarity index (S-Index) [11], which is a fast and effective algorithm, is employed to select the optimal subset.…”

Section: Dimension Reduction For the Spatial Featuresmentioning

confidence: 99%

“…Due to the complex spatial arrangement and spectral heterogeneity even within the same class, conventional spectral classification methods are inadequate for HSRM imagery [1]. It is well known that combining spatial and spectral information can improve land use classification from HSRM data [2]. Therefore, many effective spatial features concerning the structure, shape, and geometric characteristics have been proposed.…”

Section: Introductionmentioning

confidence: 99%

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Classification and Extraction of Spatial Features in Urban Areas Using High-Resolution Multispectral Imagery

Huang

Zhang

2007

IEEE Geosci. Remote Sensing Lett.

162

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Abstract-Classification and extraction of spatial features are investigated in urban areas from high spatial resolution multispectral imagery. The proposed approach consists of three steps. First, as an extension of our previous work [pixel shape index (PSI)], a structural feature set (SFS) is proposed to extract the statistical features of the direction-lines histogram. Second, some methods of dimension reduction, including independent component analysis, decision boundary feature extraction, and the similarity-index feature selection, are implemented for the proposed SFS to reduce information redundancy. Third, four classifiers, the maximum-likelihood classifier, backpropagation neural network, probability neural network based on expectationmaximization training, and support vector machine, are compared to assess SFS and other spatial feature sets. We evaluate the proposed approach on two QuickBird datasets, and the results show that the new set of reduced spatial features has better performance than the existing length-width extraction algorithm and PSI.Index Terms-Feature extraction, feature selection, high spatial resolution multispectral (HSRM) imagery, spatial feature set.

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Section: Dimension Reduction For the Spatial Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification and Extraction of Spatial Features in Urban Areas Using High-Resolution Multispectral Imagery

Huang

Zhang

2007

IEEE Geosci. Remote Sensing Lett.

162

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“…It is well known that the introduction of textural features is an effective method of addressing the classification challenge resulting from spectral heterogeneity and complex spatial arrangements within the same class [3]. In addition, spatial-spectral methods can improve the accuracy of the land-cover/use classification for remote sensing imagery [4]. The gray level co-occurrence matrix (GLCM) is a classic spatial and textural feature extraction method [5], which is widely used for texture analysis and pattern recognition for remote sensing data [3,6].…”

Section: Introductionmentioning

confidence: 99%

A Multichannel Gray Level Co-Occurrence Matrix for Multi/Hyperspectral Image Texture Representation

2014

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This study proposes a novel method for multichannel image gray level co-occurrence matrix (GLCM) texture representation. It is well known that the standard procedure for the automatic extraction of GLCM textures is based on a mono-spectral image. In real applications, however, the GLCM texture feature extraction always refers to multi/hyperspectral images. The widely used strategy to deal with this issue is to calculate the GLCM from the first principal component or the panchromatic band, which do not include all the useful information. Accordingly, in this study, we propose to represent the multichannel textures for multi/hyperspectral imagery by the use of: (1) clustering algorithms; and (2) sparse representation, respectively. In this way, the multi/hyperspectral images can be described using a series of quantized codes or dictionaries, which are more suitable for multichannel texture representation than the traditional methods. Specifically, K-means and fuzzy c-means methods are adopted to generate the codes of an image from the clustering point of view, while a sparse dictionary learning method based on two coding rules is proposed to produce the texture primitives. The proposed multichannel GLCM textural extraction methods were evaluated with four multi/hyperspectral datasets: GeoEye-1 and QuickBird multispectral images of the city of Wuhan, the well-known AVIRIS hyperspectral dataset from the Indian Pines test site, and the HYDICE airborne hyperspectral dataset from the Washington DC Mall. The results show that both the clustering-based and sparsity-based GLCM textures outperform the traditional method (extraction based on the first principal component) in terms of classification accuracies in all the experiments.

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“…In order to overcome this inadequacy, spectral features must evidently be complemented by one or the other means. It is by and large agreeable to not only use the spectral information, but also to exploit spatial analysis [2]. Spatial analytical approaches can simply be categorized into spatial features extracted by moving windows or elements and a spatial classifier based on contextual decision criteria with consideration of neighboring pixels inside the classifier [2].…”

Section: Introductionmentioning

confidence: 99%

“…It is by and large agreeable to not only use the spectral information, but also to exploit spatial analysis [2]. Spatial analytical approaches can simply be categorized into spatial features extracted by moving windows or elements and a spatial classifier based on contextual decision criteria with consideration of neighboring pixels inside the classifier [2]. This paper focuses on the spatial features to strengthen the feature space for better classification accuracies.…”

Section: Introductionmentioning

confidence: 99%

A pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery

Zhang

Huang

et al. 2006

IEEE Trans. Geosci. Remote Sensing

192

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Abstract-Shape and spectra are both important features of high spatial resolution remotely sensed (HSRRS) imagery, and they are concrete manifestation of textures on such imagery. This paper presents a spatial feature index, pixel shape index (PSI), to describe the shape feature in a local area surrounding a pixel. PSI is a pixel-based feature which measures the gray similarity distance in every direction. As merely the shape feature is inadequate for classifying HSRRS imagery, a transformed spectral feature extracted by independent component analysis is added to the input vectors of our classifier, and this replaces the original multispectral bands. Meanwhile, a fast fusion algorithm that integrates both shape and spectral features using the support vector machine has been developed to interpret the complex input vectors. The results by PSI are compared with some spatial features extracted using wavelet transform, gray level co-occurrence matrix, and the length-width extraction algorithm to test its effectiveness. The experiments demonstrate that PSI is capable of describing shape features effectively and result in more accurate classifications than other methods. While it is found that spectral and shape features can complement each other and their integration can improve classification accuracy, the transformed spectral components are also found to be more suitable for classification.Index Terms-Independent components analysis (ICA), integration of shape and spectra, shape feature, support vector machine (SVM).

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Exploiting Spectral and Spatial Information in Hyperspectral Urban Data With High Resolution

Cited by 217 publications

References 16 publications

Classification and Extraction of Spatial Features in Urban Areas Using High-Resolution Multispectral Imagery

Classification and Extraction of Spatial Features in Urban Areas Using High-Resolution Multispectral Imagery

A Multichannel Gray Level Co-Occurrence Matrix for Multi/Hyperspectral Image Texture Representation

A pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery

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